1,721,083 research outputs found
Guidelines for reusing ontologies on the semantic web
No full textThe ability to efficiently and effectively reuse ontologies is commonly acknowledged to play a crucial role in the large scale dissemination of ontologies and ontology-driven technology, being thus a pre-requisite for the ongoing realization of the Semantic Web. In this article, we give an account of ontology reuse from a process point of view. We present a methodology that can be utilized to systematize and monitor ontology engineering processes in scenarios reusing available ontological knowledge in the context of a particular application. Notably, and by contrast to existing approaches in this field, our aim is to provide means to overcome the poor reusability of existing resources — rather than to solve the more general issue of building new, more reusable knowledge components. To do so we investigate the impact of the application context of an ontology — in terms of tasks this ontology has been created for and will be utilized in — has on the feasibility of a reuse-oriented ontology development strategy and provide guidelines that take these aspects into account. The applicability of the methodology is demonstrated through a case study performed in collaboration with an international eRecruitment solution provider
Reusing ontologies on the semantic web: a feasibility study
No full textTechnologies for the efficient and effective reuse of ontological knowledge are one of the key success factors for the Semantic Web. Putting aside matters of cost or quality, being reusable is an intrinsic property of ontologies, originally conceived of as a means to enable and enhance the interoperability between computing applications. This article gives an account, based on empirical evidence and real-world findings, of the methodologies, methods and tools currently used to perform ontology-reuse processes. We study the most prominent case studies on ontology reuse, published in the knowledge-/ontology-engineering literature from the early nineties. This overview is complemented by two self-conducted case studies in the areas of eHealth and eRecruitment in which we developed Semantic Web ontologies for different scopes and purposes by resorting to existing ontological knowledge on the Web. Based on the analysis of the case studies, we are able to identify a series of research and development challenges which should be addressed to ensure reuse becomes a feasible alternative to other ontology-engineering strategies such as development from scratch. In particular, we emphasize the need for a context- and task-sensitive treatment of ontologies, both from an engineering and a usage perspective, and identify the typical phases of reuse processes which could profit considerably from such an approach. Further on, we argue for the need for ontology-reuse methodologies which optimally exploit human and computational intelligence to effectively operationalize reuse processes
A Semantically enabled architecture for crowdsourced linked data management
No full textIncreasing amounts of structured data are exposed on the Web using graph-based representation models and protocols such as RDF and SPARQL. Nevertheless, while the overall volume of such open, or easily accessible, data sources reaches critical mass, the ability of potential consumers to use them in novel applications and services is predicated on the availability of purposeful means to query and manage the data, while taking into account and mastering its essential features in terms of decentralization, heterogeneity of schema, varying quality, and scale. Many aspects of these challenges are necessarily tackled through a combination of algorithmic techniques and manual effort. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon these ideas and propose a semantically enabled architecture for crowdsourced data management systems which uses formal representations of tasks and data to automatically design and optimize the operation and outcomes of human computation projects. The architecture is applied to the context of Linked Data management to address specific challenges of LinkedData query processing such as identity resolution and ontological classification. Starting from a motivational scenario we explain how query-processing tasks can be decomposed and translated into MTurk projects using our semantic approach, and roadmap the extensions to graph-based data management technology that are required to achieve this vision
Incentive-Centric Semantic Web Application Engineering
No full textWhile many Web 2.0-inspired approaches to semantic content authoring do acknowledge motivation and incentives as the main drivers of user involvement, the amount of useful human contributions actually available will always remain a scarce resource. Complementarily, there are aspects of semantic content authoring in which automatic techniques have proven to perform reliably, and the added value of human (and collective) intelligence is often a question of cost and timing. The challenge that this book attempts to tackle is how these two approaches (machine- and human-driven computation) could be combined in order to improve the cost-performance ratio of creating, managing, and meaningfully using semantic content. To do so, we need to first understand how theories and practices from social sciences and economics about user behavior and incentives could be applied to semantic content authoring. We will introduce a methodology to help software designers to embed incentives-minded functionalities into semantic applications, as well as best practices and guidelines. We will present several examples of such applications, addressing tasks such as ontology management, media annotation, and information extraction, which have been built with these considerations in mind. These examples illustrate key design issues of incentivized Semantic Web applications that might have a significant effect on the success and sustainable development of the applications: the suitability of the task and knowledge domain to the intended audience, and the mechanisms set up to ensure high-quality contributions, and extensive user involvement
Achieving maturity: the state of practice in ontology engineering in 2009
No full textIn this paper we give an account of the current state of practice in ontology engineering based on the findings of a six months empirical survey we performed between October 2008 and March 2009 that analysed 148 ontology engineering projects from industry and academia. The survey focused on process-related issues and looked into the impact of research achievements on real-world ontology engineering projects, the complexity of particular ontology development tasks, the level of tool support, and the usage scenarios for ontologies. The main contributions of this survey compared to other works in the ontology engineering community are twofold: Firstly, the size of the data set the results are grounded on is by far larger than every other similar endeavour published in the last years. Secondly, the findings of the survey confirm the fact that ontology engineering is an established engineering discipline in respect of the maturity and level of acceptance of its main components, methodologies, methods and software tools, whereas further research should target the customization of existing technology to the specifics of vertical domains, as well as economic aspects of ontology engineering
An experiment in comparing human-computation techniques
No full textHuman computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data
Crowdsourcing tasks within linked data management
No full textMany aspects of Linked Data management – including exposing legacy data and applications to semantic formats, designing vocabularies to describe RDF data, identifying links between entities, query processing, and data curation– are necessarily tackled through the combination of human effort with algorithmic techniques. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon and extend these ideas to propose a framework by which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. Starting from a motivational scenario we introduce a set of generic tasks which may feasibly be approached using crowdsourcing platforms such as Amazon’s Mechanical Turk, explain how these tasks can be decomposed and translated into MTurk projects, and roadmap the extensions to SPARQL, D2RQ/R2R and Linked Data browsing that are required to achieve this vision
Human intelligence in the process of semantic content creation
No full textDespite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community
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